Abstract
There are several problems in the computer graphics and visualization domains which require optimization tasks to be performed in order to improve the quality of the overall process. In this context, we propose and describe an innovative optimization methodology and a supporting software framework: i-om. The design goals of the proposed framework were twofold. The first comprises the decoupling, as much as possible, of the optimization process from the application specific processing tasks. In order to attain this goal, we opted to make use of intelligent techniques (i.e. metaheuristics). The second goal is to allow remote operation, and consequently great portability and interoperability, between the optimization tools and the visualization application. To fulfill the latter requirement the proposed framework was designed with the ability to communicate with external application using a specifically developed high level message protocol. The optimization framework was implemented and the paper presents illustrative results demonstrating the usefulness and effectiveness of the proposed approach..

Abstract

Abstract
With the advent of GPU programmability, many applications have transferred computational intensive tasks into it. Some of them compute intermediate data comprised by a mixture of relevant and irrelevant elements in respect to further processing tasks. Hence, the ability to discard irrelevant data and preserve the relevant portion is a desired feature, with benefits on further computational effort, memory and communication bandwidth. Parallel stream compaction is an operation that, given a discriminator, is able to output the valid elements discarding the rest. In this paper we contribute two original algorithms for parallel stream compaction on the GPU. We tested and compared our proposals with state-of-art algorithms against different data-sets. Results demonstrate that our proposals can outperform prior algorithms. Result analysis also demonstrate that there is not a best algorithm for all data distributions and that such optimal setting is difficult to be achieved without prior knowledge of the data characteristics.

Abstract
Stream Compaction is an important task to perform in the context of data parallel computing, useful for many applications in Computer Graphics as well as for general purpose computation on graphics hardware. Given a data stream containing irrelevant elements, stream compaction outputs a stream comprised by the relevant elements, discarding the rest. The compaction mechanism has the potential to enable savings on further processing, memory storage and communication bandwidth. Traditionally, stream compaction is defined as a monotonic (or stable) operation in the sense that it preserves the relative order of the data. This is not a full requirement for many applications, therefore we distinguish between monotonic and non-monotonic algorithms. The latter motivated us to introduce the Jumping Jack algorithm as a new algorithm for non-monotonic compaction. In this paper, experimental results are presented and discussed showing that, although simple, the algorithm has interesting properties that enable it to perform faster than existent state-of-the-art algorithms, in many circumstances.

Abstract
This paper presents a description of the image generation sub-system developed to allow the presentation of realistic visual feedback in interactive visual simulation with large scene databases. The developed image generator applies all the standard state-of-art image generation algorithms aimed to real-time interactive simulation. Some of these important algorithms are also explained in this document. I addition, some innovative optimization techniques like the hierarchical back face rejection of objects, the visibility preprocessing and the automatic optimization of levels-of-detail are being developed and detailed in this paper. These techniques will allow a better use of any image generation system and improve significantly the visualization of huge scene databases even in high-end graphics architectures.

Abstract